Diffusion Models for Generative Artificial Intelligence: An Introduction for Applied Mathematicians

Generative artificial intelligence (AI) refers to algorithms that create synthetic but realistic output. Diffusion models currently offer state of the art performance in generative AI for images. They also form a key component in more general tools, including text-to-image generators and large langu...

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Bibliographic Details
Published in:arXiv.org 2023-12
Main Authors: Higham, Catherine F, Higham, Desmond J, Grindrod, Peter
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Generative artificial intelligence
Image processors
Large language models
Machine learning
Mathematicians
Partial differential equations
Stochastic processes
Students
Undergraduate study
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Summary:Generative artificial intelligence (AI) refers to algorithms that create synthetic but realistic output. Diffusion models currently offer state of the art performance in generative AI for images. They also form a key component in more general tools, including text-to-image generators and large language models. Diffusion models work by adding noise to the available training data and then learning how to reverse the process. The reverse operation may then be applied to new random data in order to produce new outputs. We provide a brief introduction to diffusion models for applied mathematicians and statisticians. Our key aims are (a) to present illustrative computational examples, (b) to give a careful derivation of the underlying mathematical formulas involved, and (c) to draw a connection with partial differential equation (PDE) diffusion models. We provide code for the computational experiments. We hope that this topic will be of interest to advanced undergraduate students and postgraduate students. Portions of the material may also provide useful motivational examples for those who teach courses in stochastic processes, inference, machine learning, PDEs or scientific computing.
ISSN:2331-8422